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<p><span class="big">The pattern.nl module contains a fast part-of-speech tagger for Dutch (identifies nouns, adjectives, verbs, etc. in a sentence), sentiment analysis, and tools for Dutch verb conjugation and noun singularization &amp; pluralization.</span></p>
<p>It can be used by itself or with other&nbsp;<a href="pattern.html">pattern</a>&nbsp;modules:&nbsp;<a href="pattern-web.html">web</a>&nbsp;|&nbsp;<a href="pattern-db.html">db</a>&nbsp;| <a href="pattern-en.html">en</a>&nbsp;|&nbsp;<a href="pattern-search.html">search</a>&nbsp;|&nbsp;<a href="pattern-vector.html">vector</a>&nbsp;|&nbsp;<a href="pattern-graph.html">graph</a>.</p>
<p><img src="../g/pattern_schema_nl.gif" alt="" width="620" height="180" /></p>
<hr />
<h2>Documentation</h2>
<p>The functions in this module take the same parameters and return the same values as their counterparts in <a href="pattern-en.html">pattern.en</a>. Refer to the documentation there for more details.&nbsp;&nbsp;</p>
<h3>Noun singularization &amp; pluralization</h3>
<p>For Dutch nouns there is <span class="inline_code">singularize()</span> and <span class="inline_code">pluralize()</span>.&nbsp;The implementation is slightly less robust than the English version&nbsp;(accuracy 91% for singularization and 80% for pluralization).</p>
<div class="example">
<pre class="brush:python; gutter:false; light:true;">&gt;&gt;&gt; from pattern.nl import singularize, pluralize
&gt;&gt;&gt;  
&gt;&gt;&gt; print singularize('katten')
&gt;&gt;&gt; print pluralize('kat')

kat
katten </pre></div>
<h3>Verb conjugation</h3>
<p>For Dutch verbs there is <span class="inline_code">conjugate()</span>, <span class="inline_code">lemma()</span>, <span class="inline_code">lexeme()</span> and <span class="inline_code">tenses()</span>.&nbsp;The lexicon for verb conjugation contains about 4,000 common Dutch verbs. For unknown verbs it will fall back to a rule-based approach with an accuracy of about 81%.</p>
<div class="example">
<pre class="brush:python; gutter:false; light:true;">&gt;&gt;&gt; from pattern.nl import conjugate
&gt;&gt;&gt; from pattern.nl import INFINITIVE, PRESENT, SG
&gt;&gt;&gt;  
&gt;&gt;&gt; print conjugate('ben', INFINITIVE)
&gt;&gt;&gt; print conjugate('ben', PRESENT, 2, SG) 

zijn
bent </pre></div>
<h3>Attributive &amp; predicative adjectives&nbsp;</h3>
<p>Dutch adjectives followed by a noun&nbsp;inflect with an&nbsp;<span class="inline_code">-e</span>&nbsp;suffix (e.g., <em>braaf</em>&nbsp;→ <em>brave kat</em>). You can get the base form with the <span class="inline_code">predicative()</span> function, or vice versa with&nbsp;<span class="inline_code">attributive()</span>. Accuracy is 99%.</p>
<div class="example">
<pre class="brush:python; gutter:false; light:true;">&gt;&gt;&gt; from pattern.nl import attributive, predicative
&gt;&gt;&gt;  
&gt;&gt;&gt; print predicative('brave') 
&gt;&gt;&gt; print attributive('braaf') 

braaf
brave </pre></div>
<h3 class="example">Sentiment analysis</h3>
<p class="example">For opinion mining there is <span class="inline_code">sentiment()</span>, which returns a (<span class="inline_code">polarity</span>, <span class="inline_code">subjectivity</span>)-tuple, based on a lexicon of adjectives.&nbsp;Polarity is a value between <span class="inline_code">-1.0</span> and <span class="inline_code">+1.0</span>, subjectivity between <span class="inline_code">0.0</span> and <span class="inline_code">1.0</span>. The accuracy is around 82% (P 0.79, R 0.86) for book reviews:</p>
<div class="example">
<pre class="brush:python; gutter:false; light:true;">&gt;&gt;&gt; from pattern.nl import sentiment
&gt;&gt;&gt; print sentiment('Een onwijs spannend goed boek!')

(0.69, 0.90) </pre></div>
<h3>Parser</h3>
<p>For parsing there is <span class="inline_code">parse()</span>, <span class="inline_code">parsetree()</span> and <span class="inline_code">split()</span>. The <span class="inline_code">parse()</span> function annotates words in the given string with their part-of-speech <a class="link-maintenance" href="mbsp-tags.html">tags</a> (e.g., <span class="postag">NN</span> for nouns and <span class="postag">VB</span> for verbs). The parsetree() function takes a string and returns a tree of nested objects (<span class="inline_code">Text</span> → <span class="inline_code">Sentence</span> → <span class="inline_code">Chunk</span> → <span class="inline_code">Word</span>). The <span class="inline_code">split()</span> function takes the output of <span class="inline_code">parse()</span> and returns a <span class="inline_code">Text</span>. See the pattern.en documentation (<a class="link-maintenance" href="pattern-en.html#tree">here</a>) how to manipulate <span class="inline_code">Text</span> objects.&nbsp;</p>
<div class="example">
<pre class="brush:python; gutter:false; light:true;">&gt;&gt;&gt; from pattern.nl import parse, split
&gt;&gt;&gt; 
&gt;&gt;&gt; s = parse('De kat zit op de mat.')
&gt;&gt;&gt; for sentence in split(s):
&gt;&gt;&gt;     print sentence 

Sentence('De/DT/B-NP/O kat/NN/I-NP/O zit/VBZ/B-VP/O op/IN/B-PP/B-PNP'
         'de/DT/B-NP/I-PNP mat/NN/I-NP/I-PNP ././O/O')</pre></div>
<p>The parser is built on Jeroen Geertzen's <a href="http://cosmion.net/jeroen/software/brill_pos/" target="_blank">Dutch language model</a>.&nbsp;The accuracy is around 91%. The original&nbsp;<a href="http://lands.let.ru.nl/literature/hvh.1999.2.ps" target="_blank">WOTAN</a> tagset&nbsp;is mapped to <a href="mbsp-tags.html">Penn Treebank</a>. If you need to work with the original tags you can also use&nbsp;<span class="inline_code">parse()</span> with an optional parameter <span class="inline_code">tagset="WOTAN"</span>.</p>
<p class="small"><span style="text-decoration: underline;">Reference</span>: Geertzen, J. (2010).&nbsp;<em>Brill-NL. </em>Retrieved from: <a class="noexternal" style="color: inherit;" href="http: //cosmion.net/jeroen/software/brill_pos/" target="_blank">http: //cosmion.net/jeroen/software/brill_pos/</a>.</p>
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